Hydronic Heating Retrofits for 

Low-Rise Multifamily Buildings:

Boiler Control Replacement and 

Monitoring

Jordan Dentz, Hugh Henderson, and Kapil Varshney

Advanced Residential Integrated Energy Solutions 

Collaborative

September 2014
This report received minimal editorial review at NREL

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Hydronic Heating Retrofits for Low-Rise Multifamily Buildings:

Boiler Control Replacement and Monitoring

Prepared for:

The National Renewable Energy Laboratory

On behalf of the U.S. Department of Energy’s Building America Program

Office of Energy Efficiency and Renewable Energy

15013 Denver West Parkway

Golden, CO 80401

NREL Contract No. DE-AC36-08GO28308

Prepared by:

Jordan Dentz, Hugh Henderson, and Kapil Varshney

ARIES Collaborative

The Levy Partnership, Inc., 1776 Broadway, Suite 2205

New York, NY 10019

NREL Technical Monitor: Michael Gestwick

Prepared Under Subcontract No. KNDJ-0-40347-04

September 2014
The work presented in this report does not represent 

performance of any product relative to regulated 

minimum efficiency requirements.

The laboratory and/or field sites used for this work are 

not certified rating test facilities. The conditions and 

methods under which products were characterized for 

this work differ from standard rating conditions, as 

described.

Because the methods and conditions differ, the reported 

results are not comparable to rated product performance 

and should only be used to estimate performance under 

the measured conditions.
iii

Contents

List of Figures ............................................................................................................................................ iv

List of Tables ............................................................................................................................................... v

Definitions................................................................................................................................................... vi

Acknowledgments .................................................................................................................................... vii

Executive Summary................................................................................................................................. viii

1 Introduction ............................................................................................................................................ 1

2 Relevance to Building America’s Goals .............................................................................................. 3

3 Site Description...................................................................................................................................... 4

4 Retrofit Strategies and Monitoring Plan .............................................................................................. 8

4.1 Building 3.............................................................................................................................9

4.2 Building 4...........................................................................................................................12

4.3 Building 55.........................................................................................................................12

4.4 Data Collection ..................................................................................................................13

4.5 Equipment..........................................................................................................................15

5 Methodology......................................................................................................................................... 17

6 Results and Analysis ........................................................................................................................... 18

6.1 Building 3...........................................................................................................................18

6.1.1 Analysis of the Impact of Indoor Cutoff .................................................................19

6.1.2 Utility Bill Analysis.................................................................................................21

6.2 Building 4...........................................................................................................................23

6.2.1 Utility Bill Analysis for Building 4.........................................................................23

6.3 Building 55.........................................................................................................................25

6.3.1 Boiler Runtime—Gas Use Correlation....................................................................26

6.3.2 Boiler Runtime Analysis.........................................................................................27

6.3.3 Utility Bill Analysis.................................................................................................29

6.4 Cost Effectiveness..............................................................................................................31

6.5 Uncertainties......................................................................................................................32

6.6 Other Impacts.....................................................................................................................33

7 Conclusions.......................................................................................................................................... 34

References................................................................................................................................................. 35

Appendix A ................................................................................................................................................ 36

Appendix B ................................................................................................................................................ 38
iv

List of Figures

Figure 1. Exterior view of Building 4 and typical basement boiler room.............................................. 4

Figure 2. Pre-existing boiler controllers (a, b) and local radiator controller (c, d)............................... 5

Figure 3. Approximate previously existing boiler reset schedules ....................................................... 6

Figure 4. Aerial photo showing divisions of buildings and number of units ....................................... 6

Figure 5. Typical space heating system diagram .................................................................................... 7

Figure 6. Controller Web interface .......................................................................................................... 10

Figure 7. Mixing valve controller (top) and boiler controller (bottom) Web interface ....................... 10

Figure 8. Sample apartment temperature sensor data.......................................................................... 11

Figure 9. Building 3 system configuration ............................................................................................. 11

Figure 10. Building 4 system configuration ........................................................................................... 12

Figure 11. Building 55 system configuration ......................................................................................... 13

Figure 12. Building 3 controller reset schedules................................................................................... 18

Figure 13. Building 3 dependence of boiler runtime on indoor temperature cutoff at night only... 20

Figure 14. Building 3 average indoor temperature (summer data removed)...................................... 21

Figure 15. Building 3 dependence of energy consumption on OAT ................................................... 22

Figure 16. Building 4 controller reset schedules................................................................................... 23

Figure 17. Building 4 dependence of energy consumption on OAT ................................................... 24

Figure 18 Indoor air temperature profiles in various apartments in Building 4 ................................. 25

Figure 19. Building 55 controller reset schedules................................................................................. 26

Figure 20. Plot of actual and predicted gas use .................................................................................... 27

Figure 21. Plot of boiler loading—comparing days with and without setback through January 31, 

2014 for Building 55 ........................................................................................................................... 28

Figure 22. Indoor air temperature profiles in various apartments in Building 55 .............................. 29

Figure 23. Building 55 dependence of energy consumption on OAT ................................................. 30

Figure 24. Building 55 dependence of energy consumption on night time setback ......................... 31

Figure 25. Building 3 system temperature versus outdoor temperature ............................................ 39

Figure 26. Building 3 system temperature versus average indoor temperature................................ 39

Figure 27. Building 3 apartment temperatures from control system sensors: October 2012 – May 

2013; with indoor cutoff activated .................................................................................................... 40

Figure 28. Building 3 apartment temperatures from control system sensors: October 2013 –

December 2013; indoor cutoff mode deactivated ........................................................................... 40

Figure 29. Boiler loading, Building 4....................................................................................................... 41

Figure 30. Gas use, Building 4................................................................................................................. 42

Figure 31. No impact of nighttime setback, Building 4 ......................................................................... 42

Figure 32. Boiler (system) supply target and outdoor temperature in Building 55............................ 43

Unless otherwise indicated, all figures were created by ARIES.
v

List of Tables

Table 1. Existing and Planned Retrofit Controllers ................................................................................. 8

Table 2. Pre- and Post-Retrofit Boiler Controller Settings...................................................................... 9

Table 3. Summary of Monitoring Data Points in Building 3.................................................................. 14

Table 4. Summary of Monitoring Data Points in Building 4.................................................................. 14

Table 5. Summary of Monitoring Data Points in Building 55................................................................ 15

Table 6. Materials and Equipment........................................................................................................... 16

Table 7. Data Collection Periods for Building 3..................................................................................... 19

Table 8. Building 3 Effect of Indoor Cutoff on Boiler Runtime............................................................. 20

Table 9. Dependence of Boiler Runtime on OAT With and Without Indoor Temperature Cutoff 

Enabled for Building 3 ....................................................................................................................... 20

Table 10. Building 3 Average Indoor Temperature With and Without Indoor Cutoff Enabled......... 21

Table 11. Building 3 Dependence of Energy Consumption on OAT (Slope) and Coefficient of 

Determination (R2

) for Building 3...................................................................................................... 22

Table 12. Building 3 Pre- and Post-Retrofit Heating and Total Energy Consumption and Reduction 

(therms) for Building 3 ....................................................................................................................... 23

Table 13. Building 4 Dependence of Energy Consumption on OAT and Coefficient of 

Determination (R2

) for Building 4...................................................................................................... 24

Table 14. Building 4 Pre- and Post-Retrofit Heating and Total Energy Consumption and Reduction 

(therms) ............................................................................................................................................... 25

Table 15. Data Collection Periods for Building 55................................................................................. 26

Table 16. Actual and Predicted Gas Use by Utility Billing Period for Building 55 ............................. 27

Table 17. Dependence of Boiler Runtime on Nighttime Setback (Slope) and Coefficient of 

Determination (R2

) for Building 55.................................................................................................... 28

Table 18. Dependence of Energy Consumption on OAT (Slope) and Coefficient of Determination 

(R2

) for Building 55 ............................................................................................................................. 30

Table 19. Pre- and Post-Retrofit Heating and Total Energy Consumption and Reduction (therms) 

for Building 55..................................................................................................................................... 30

Table 20. Energy and Cost Savings Calculations for Heating Season 2011–2012............................. 32

Table 21. Periods in Which One of the Boilers in Building 3 Was Running Uncontrollably ............. 36

Unless otherwise indicated, all tables were created by ARIES.
vi

Definitions

ARIES Advanced Residential Integrated Energy Solutions Building America team

CAST Cambridge Alliance for Spanish Tenants

DHW Domestic hot water

F Fahrenheit

HRI Homeowners’ Rehab, Inc.

OAT Outdoor air temperature

ORC Outdoor reset control

TRV Thermostatic radiator valve

WWSD Warm weather shutdown
vii

Acknowledgments

The Advanced Residential Integrated Energy Solutions (ARIES) Collaborative would like to 

recognize the support of the U.S. Department of Energy’s Building America Program and 

Michael Gestwick of the National Renewable Energy Laboratory for technical guidance. This 

project would not have been possible without the support and participation on Jane Carbone and 

Beverly Craig of Homeowners’ Rehab Inc. (HRI), Cambridge, Massachusetts; Martha Abrams 

and Ivan Leslie of Abrams Management, Boston, Massachusetts; and Kevin Reid of Boston 

Cooling and Heating, Norwood, Massachusetts. Thanks are also due to Andrew Proulx, 

EnerSpectives, Inc., Concord, Massachusetts, Victor Zelmanovich, Intech 21, Port Washington, 

New York, and Michael Keber, Tekmar Controls, Vernon, British Columbia.
viii

Executive Summary

The ARIES Collaborative, a U.S. Department of Energy Building America research team, 

partnered with Neighbor Works America affiliate HRI of Cambridge, Massachusetts to 

implement and study improvements to the central hydronic heating systems in one of the 

nonprofit’s housing developments. The heating control systems in the three-building, 42-unit 

Columbia Cambridge Alliance for Spanish Tenants (CAST) housing development were upgraded 

in an effort projected to reduce heating costs 15%–25%.

HRI recognized that heating fuel use per square foot per heating degree day in the development 

was excessive compared to its other properties of similar construction. Although a poorly 

insulated thermal envelope contributes to high energy bills, adding insulation to the exterior 

walls was not a cost-effective or practical option for Columbia CAST, given the desire to 

maintain the building’s historic exterior and to avoid disrupting the residents. A more costeffective and readily available option was improving heating system efficiency. 

Efficient operation of the heating system faced several obstacles, including inflexible boiler 

controls, failed thermostatic radiator valves, and disregard by residents of recommended 

thermostat set points. Boiler controls in all three buildings were replaced with systems that offer 

temperature setbacks and one that controls heat delivery based on apartment temperatures in 

addition to outdoor temperatures. This is the final report of a 3-year project, including two and 

one half winter monitoring seasons. During the first season various control settings and system 

configurations were altered as the systems were adjusted to maximize comfort and energy 

savings. During the second and third seasons, control settings were adjusted a few times on 

schedules intended to provide data to compare various techniques, including indoor temperature 

controls and nighttime setbacks. 

A utility bill analysis shows that after implementing control techniques, overall weathernormalized energy consumption for heating was reduced by approximately 10%–31% and the 

average savings across the three buildings was approximately 19%. Indoor temperature cutoff 

was estimated to reduce boiler runtime (and by extension heating fuel consumption) by 28% in 

the one building in which it was implemented. Daytime and nighttime data were analyzed 

separately because they had different indoor cutoff thresholds and different reset curves. Nearly 

all the savings were obtained in the nighttime, which had a lower indoor temperature cutoff 

(68°F) compared to daytime (73°F). This implies that the outdoor reset curve selection was 

appropriately adjusted for this building for daytime operation. Nighttime setback of heating 

system supply water temperature had no discernable impact on boiler runtime or gas bills.
1

1 Introduction

Heating cost reductions can be achieved in several ways, including improving boiler control 

strategy, giving the resident or building manager the ability to more precisely modulate the 

temperature according to need (instead of opening a window), and altering the distribution of 

heat in the building in ways that better reflect demand.

A number of studies exist, documenting the benefits of outdoor reset control (ORC) in 

multifamily buildings compared to the aquastat-controlled constant water temperatures 

(sometimes with controls that turn off the boiler when outdoor temperatures exceed a certain 

threshold) that typified the previous generation of multifamily heating systems (Hewett and 

Peterson 1984; Peterson 1986). ORCs alone can improve the overall performance of the heating 

system, but they are very sensitive to commissioning. If the compensation (or reset) curve is not 

adjusted properly, the overall heating energy consumption can be higher than that of a boiler 

controlled at a constant water temperature. Adding thermostatic radiator valves (TRVs) to 

radiators can potentially reduce overheating. However, the effectiveness of TRVs depends on 

proper use by tenants (Liao and Dexter 2004) and equipping an entire building with TRVs can be 

costly. It has been shown that the overall performance of a heating system is highly dependent on 

the algorithm for determining the boiler temperature set point. Inferential models that, in the 

absence of real-time data, predict the average indoor temperature based on a simplified physical 

model have been shown to be effective at increasing heating system efficiency (Liao and Dexter 

2004; Liao and Dexter 2005). Now another shift in control strategies is underway, one based on 

measured real-time average indoor temperatures in combination with outdoor temperatures 

(Center for Energy and the Environment 2006; CNT Energy 2010; Gifford 2004).

New wireless technologies are available to cost-effectively monitor indoor space temperatures, 

centralize and automate thermostat set points, and, with the requisite level of control points in 

place, dynamically adjust heat distribution patterns. Control system manufacturers have 

produced case studies claiming benefits from ORC with indoor space temperature-based cutoffs

of 25%–40% savings in heating fuel use. However, existing conditions and control algorithms 

are typically not well documented in these case studies. No known third-party, independent 

studies exist quantifying the effects of these systems.

Outdoor reset control is a popular type of multifamily boiler control strategy with variants for 

both steam and hot water heating. ORC has existed for perhaps as long as 50 years. It has been 

more prevalent in multifamily buildings, but is now becoming more common in single family 

residential systems in part because of legislation that went into effect in 2012 requiring improved 

boiler controls. ORC is one way to meet the requirements (Woerpel, 2012). 

The basic concept behind ORC is that the amount of heat delivered to the building should vary in 

proportion to the outdoor temperature. For hot water systems, this takes the form of varying the 

supply water temperature from the boiler. For steam-heated buildings, this takes the form of 

varying the duration of the steam cycle (number of minutes per hour that steam is provided to 

heat emitters). In mild weather, proportionally lower water temperatures and less steam runtime 

allow heating systems with ORC to limit overheating and reduce fuel consumption. Lower water 

temperatures also reduce distribution losses from hydronic systems.
2

Multifamily hot water space heating systems can have multiple boilers, circulation loops, pumps,

and valves. Reset control can be integrated with these components to achieve the desired supply 

temperature to the building.

The underlying logic behind ORC is contained in the reset curve or ratio. It specifies the 

variation of water temperature (boiler, system supply, system return, or other) with outdoor 

temperature. The steeper the curve (greater the slope’s magnitude), the sharper the drop in water 

temperature with each degree rise in outdoor temperature. The reset ratio is usually linear (but 

some control manufacturers use proprietary nonlinear algorithms). In addition to adjusting the 

slope of the reset curve, the reset curve can be offset up or down depending on the heat loss 

characteristics of the building and to implement a setback such as for nighttime or vacation mode 

in single-family homes.

One caveat with lowering water temperature is that many boilers, especially older ones, are not 

designed to accept return water temperatures below 120°–130°F for extended periods of time. 

Risks of this include possible condensation of corrosive flue gasses that can over time corrode 

the heat exchanger. 

Condensing boilers, on the other hand, are well suited to accept low return water temperatures, 

and therefore take maximum advantage of ORC, because they are designed with materials that 

can withstand these conditions without deterioration. Other strategies such as mixing valves or 

injection pumps can maintain high return water temperature by recirculating some supply water 

or injecting controlled amounts of boiler water into the boiler return.
3

2 Relevance to Building America’s Goals

There is a large stock of multifamily buildings in the Northeast and Midwest with space heating 

provided by centralized hot water or steam. According to the 2005 American Housing Survey, 

there are about 3.2 million occupied hydronically heated, low-rise multifamily housing units in 

the United States (U.S. Census Bureau 2006). Nearly 90% of these homes are in the Northeast or 

Midwest, with a large portion being rental units (40%) or occupied by the elderly (24%). Most 

hydronically heated homes are older, with only 1% being classified as new construction (built 

within the past 4 years) in the 2005 American Housing Survey data. Many of these housing units 

are candidates for improved boiler controls. Vendors using established technologies are currently 

well suited to offer boiler controls on a widespread basis.

Typically, residents of these buildings do not pay for heat directly (heat is not submetered). 

Losses from these systems are often higher than would be expected for buildings with centralized 

heat provided by a boiler serving multiple units (a significant number of apartments are 

overheated much of the time (Dentz, Varshney, & Henderson, 2013)). Upgrades to these heating 

systems often include the installation of new, higher performance boilers, yet heating costs 

sometimes remain high because spaces are too warm and the thermal distribution systems are 

inefficient. Major underlying problems are: (1) outmoded and inefficient boiler control 

strategies, and (2) the inability to regulate the amount of heat provided at the point of use (the 

radiator). 

In this project, the relative effectiveness of control strategies to improve hydronic space heating 

performance in three low-rise multifamily buildings is evaluated. The research questions 

addressed are: 

1. What are the energy and comfort impacts of retrofitting multifamily central boiler 

controllers using ORC? 

2. How does a control system incorporating apartment temperature data compare in cost and 

performance with well-tuned ORC strategies?

3. What is the impact on space heating energy consumption of nighttime setback of supply 

water temperature on multifamily buildings with central hydronic space heating?

The results of this work could be included in a future measures guidelines on hydronic heating 

system retrofits for multifamily buildings.
4

3 Site Description

Homeowners’ Rehab Inc.’s (HRI) Columbia Cambridge Alliance for Spanish Tenants (CAST)

housing development is a 42-unit complex of three, three-story masonry buildings in Cambridge, 

Massachusetts (Figure 1). Gas, which is used for space heating, water heating, cooking, and in 

one building for laundry, accounts for about 80% of the annual property energy expenditures. 

Each apartment is metered for electricity directly by the utility; electricity use is not presented in 

this report. 

Figure 1. Exterior view of Building 4 and typical basement boiler room

Gas use in the buildings was higher than in other buildings in the area owned by HRI. Gas use 

for space heating alone was more than 0.6 therms/ft2/yr (more than 0.8 therms/ft2/yr overall).1

Other HRI buildings with gas heat use 0.36–0.65 therms/yr overall. While a poorly insulated 

thermal envelope contributes to the problem, insulating the masonry walls from the exterior was 

not an option because of cost and historic preservation restrictions. Insulating the walls on the 

interior was also not an option because of the cost and disruption created by interior construction 

work. Other envelope efficiency measures may be considered in the future, but are unlikely to 

improve heating distribution.

The buildings are heated with multiple boilers and controllers that reset supply water temperature 

based on outdoor temperature (Figure 2). The building operators are obligated under local 

ordinance to maintain a minimum space temperature in each apartment of 68°F during the day 

and 64°F at night during heating season (Massachusets Department of Public Health, 2012). 

Each apartment has one or two nonelectric actuator zone valve controllers to regulate water flow 

through the baseboard heaters (Figure 2 (c) and (d)). These valves, when functional (many are 

failed), allow the resident some control over heating and are marked with temperatures, although 

their calibration is unknown.

 1 Analysis of the building by Wegowise.com indicates that prior to the retrofit their heating consumption was 11–12 

Btu/ft2

/heating degree day, rating as “poor,” with 4–5 considered “great” and 8 considered “good.”
5

(a) (b)

(c) (d)

Figure 2. Pre-existing boiler controllers (a, b) and local radiator controller (c, d)

Figure 3 shows the approximate boiler reset schedules previously used in two of the buildings

(Building 3 and Building 55). The third building (Building 4) had a more modern Tekmar 

controller set more aggressively to reduce the hot water supply temperature. The 1990s vintage 

Weil McLain controllers were set on a less aggressive slope. On April 7, 2011 when outdoor 

temperatures were 45°–50°F, the supply temperature was about 30°F higher in Building 3 than in 

Building 4. This is consistent with the finding (based on utility bill analysis) that Building 3 

consumed 17% more space heating fuel per square foot than Building 4. The large data points in 

Figure 3 represent the settings on that day. The lines define the schedules for the respective 

controllers.
6

Figure 3. Approximate previously existing boiler reset schedules

An aerial view of the property indicating divisions between buildings is shown in Figure 4. Each 

building is composed of three attached sections, each with its own address number. Each section 

contains one or two apartments on each of three floors. Heating system risers are located under 

each line of radiators in the front and back sides of the buildings with one riser serving radiators

in the first-floor apartment and a second riser serving radiators on both the second and third

floors.

Figure 4. Aerial photo showing divisions of buildings and number of units2

 2 Note that each building is composed of three attached sections, each with its own address; the buildings are 

referred to in this report using one of these addresses (3, 4, or 55).

Reset ratio 0.5

Reset ratio 0.8
7

Each building has a boiler room in the central portion of the basement. Buildings 3 and 4 (each 

row of building sections is referred to by its lowest address number) both have three 87% annual 

fuel utilization efficiency 175,000 Btu/h input boilers supplying space heating. Building 55 has 

two 87% annual fuel utilization efficiency space heating boilers. Each building also has an 

additional boiler dedicated to supplying domestic hot water (DHW). Figure 5 shows a system 

diagram typical of the three buildings. Two pumps circulate the water in the primary loop, one 

operating at a time.

Figure 5. Typical space heating system diagram
8

4 Retrofit Strategies and Monitoring Plan

Boiler controls were replaced in all three Columbia CAST buildings as part of the retrofit. Table 

1 describes the pre-existing control systems and retrofit measures. Table 2 describes the pre- and 

post-retrofit boiler controller settings.

Table 1. Existing and Planned Retrofit Controllers

During the first heating season (2011–2012), various control settings and system configurations 

were altered as the systems were adjusted to maximize comfort and energy savings. During the 

second and third seasons, control settings were adjusted a few times on schedules intended to 

provide data to compare various techniques, including indoor temperature controls and nighttime 

setbacks.

 3 Additional energy savings could be achieved if the supply water temperature is allowed to follow the reset curve 

down to lower temperatures rather than bottoming out at 140°F.

Buildings by Addresses

Building 3

(18 Apartments)

Building 4

(15 Apartments)

Building 55

(9 Apartments)

Boiler Quantity

and Age at Time 

of Retrofit (2011)

(3) 8 years (3) 3 years (2) 1.5 years

Original Boiler 

Controller Weil-McLain System 1 Tekmar 264 Weil-McLain 

System 1

Boiler Return 

Water 

Temperature

Boilers designed to 

operate at a return water 

temperature ≥ 140°F. 

This limits the potential 

efficacy of the 

controller’s outdoor 

temperature reset 

function.3

Boilers designed to 

operate with a return 

water temperature 

≥ 140°F. This limits the 

potential efficacy of the 

controller’s outdoor 

temperature reset 

function.

Boilers have a builtin bypass to allow 

building return 

water temperature as 

low as 60°F.

Boiler Controls

Replaced with Intech 21 

controller and indoor 

temperature sensors in 

every apartment to limit 

heating when average 

indoor temperature 

reaches set point.

Replaced with Tekmar 

274 controller capable 

of night setbacks. No 

indoor temperature 

sensors.

Replaced with

Tekmar 274 

controller capable of 

night setbacks. No 

indoor temperature 

sensors.

Retrofit Mixing 

Valve

Added new 3-way 

mixing valve and 

controller (Intech 21)

Added new 3-way 

mixing valve and 

controller (Tekmar 

362—eventually 

abandoned

No mixing valve 

added
9

Table 2. Pre- and Post-Retrofit Boiler Controller Settings

4.1 Building 3

In Building 3, the new boiler control system allows for remote tracking and control of all 

parameters, as well as setbacks.4 It also incorporates wireless temperature sensors in all 

apartments that provide input into the control algorithm. When the average of the indoor sensor 

readings exceeds the indoor set point by the dead band (set to 1°F), the controller shuts off the 

space heating boilers. The system utilizes hardware from Intech 21, a company that specializes in 

self-healing wireless networks and heating system controls. The central controller communicates 

with an offsite server that stores logged temperature and boiler operation data and makes these 

historical data available on a website. The Web-based system allows remote operation and 

 4 Setbacks are implemented by setting back the building supply water temperature.

Parameter (°F) Building 3 Building 4 Building 55

Period 2010–2011 Postretrofit 2010–2011 Postretrofit 2010–2011 Postretrofit

Night Setback 0 0 0 0 and 5 0 0, 5, and 

10

Reset Curve 

System Supply @ 

OAT Minimum

180 170 180 180 180 170 and 

180

Reset Curve OAT 

Minimum 0 10 10 10 and 

15 0 10 and 15

Reset Curve 

System Supply @ 

OAT Maximum

145 130 130 70 145 70

Reset Curve OAT 

Maximum 70 50 50 70 and 

82 70 70

Reset Curve 

Ratio 0.5 1.0 0.8 1.83 and 

1.64 0.5 1.67 and 2

Minimum System 

Supply 

Temperature

Unknown 110–140 130 110 and 

120 Unknown 110 and 120

Indoor Cutoff 

Day n/a 73 n/a n/a n/a n/a

Indoor Cutoff 

Night n/a 68 n/a n/a n/a n/a

Warm Weather 

Shutdown 

(WWSD) Day

65 55 65 65 65 65

WWSD Night 65 45 65 65 65 65
10

modification of the control parameters and provides real-time access to apartment temperature 

data so that building operators can ensure the legally required minimum heating temperature is 

provided to each apartment, without requiring an excessively large safety factor. One risk of this 

system is that tenants using supplemental heating (or cooling) could affect temperature sensor 

readings inadvertently or intentionally in an effort to obtain more (or less) heat. None of this 

behavior was observed, and the averaging of all apartment temperatures minimizes the impact 

any one apartment can have on the system.

Access to these data also assists in diagnosing heating system problems and addressing tenant 

complaints. Figure 6 shows the controller Web interface; Figure 7 shows the boiler and mixing 

valve control modes, set points, and temperatures; and Figure 8 shows sample apartment 

temperature data. Building management and the HVAC contractor were made aware of these 

functions but worked through ARIES researchers to obtain system data in order to prevent 

changes that could have affected the research. 

A three-way mixing valve was added to the heating system piping (Figure 9). This successfully 

maintained return water temperatures above the levels that could potentially damage the existing 

noncondensing boilers (140°F).

Figure 6. Controller Web interface

Figure 7. Mixing valve controller (top) and boiler controller (bottom) Web interface
11

Figure 8. Sample apartment temperature sensor data

Figure 9. Building 3 system configuration
12

4.2 Building 4

In Building 4, new boiler controls allow for setbacks and ORC as well as remote monitoring of 

all parameters. A three-way mixing valve was added (Figure 10) to maintain return water 

temperatures above the levels that could potentially damage the existing noncondensing boilers 

(140°F); however, as discussed in Appendix B this did not function as intended and was 

abandoned.

Figure 10. Building 4 system configuration

4.3 Building 55

The new controller in Building 55 is capable of setbacks. The boilers in Building 55 have a builtin bypass to mix hot water from the boiler outlet with colder return water (as low as 60°F) from 

the building prior to entry to the boiler sections when needed to prevent thermal shock and 

condensation of flue gases in the boilers’ heat exchangers. Therefore the addition of a new threeway mixing valve was unnecessary (Figure 11).
13

Figure 11. Building 55 system configuration

4.4 Data Collection

Data to evaluate energy savings and comfort impacts were collected under various control 

scenarios for each building. Table 3, Table 4, and Table 5 summarize the data collection in 

Buildings 3, 4, and 55, respectively. Location codes in the tables refer to Figure 9, Figure 10, and 

Figure 11.
14

Table 3. Summary of Monitoring Data Points in Building 3

Table 4. Summary of Monitoring Data Points in Building 4

 5 The % indicates the runtime of the boiler. Because the boilers are single stage, runtime is proportional to capacity.

Data 

Point 

Name

Description Location

(see Figure 9)

Engineering 

Units

TZi Temperature in apartment using networked 

sensors

All 18 apartments °F

TZj Temperatures in apartments using standalone 

sensors

15 sensors in 

5 apartments °F

TBP Temperature of bypass water B1 °F

TAO Temperature outdoor air (used by controller) Outside °F

TWS Temperature of hot water supplied to building S1 °F

TWR Temperature of hot water returned from 

building

R1 °F

TBR Temperature of hot water entering the boilers S2 °F

SB1-3 Cumulative runtime on boilers Each of 3 boilers Hours

VLV Mixing valve position VLV % open

NG Natural gas usage from monthly utility billing Wegowise Therms

Data 

Point 

Name

Description Location

(see Figure 10)

Engineering 

Units

TZj Temperatures in apartments using standalone 

sensors

15 sensors in 

4 apartments °F

TBS4 Boiler supply temperature S °F

TBR4 Boiler return temperature R °F

TAO4 Temperature outdoor air (used by controller) Outside °F

TWS4 Temperature of hot water supplied to building T2 °F

TWR4 Temperature of hot water returned from 

building

T1 °F

TBT4 Target supply temperature for boilers 274 set point °F

SB4 Boiler plant operation percentage5 0, 33, 66, 100 %

NG Natural gas usage from monthly utility billing Wegowise therms
15

Table 5. Summary of Monitoring Data Points in Building 55

One networked temperature sensor is installed in a central location on an interior wall in each 

apartment in Building 3 (data point TZi). These sensors provide data to the boiler controller for 

the indoor temperature cutoff function. Accuracy was checked by a handheld temperature sensor 

at three locations in each apartment in November 2011 and March 2012 and the system was 

calibrated accordingly. 

Firing or runtime for each single-stage boiler is totaled for each 15-minute period. This was 

compared to monthly gas use to corroborate the boiler input rates. This information was used to 

predict gas use for more finely resolved time intervals (daily, hourly, 15-minute) than are 

available from the utility bills.

The Columbia CAST buildings utilize Wegowise.com, a system that automatically tracks 

monthly utility use and costs and uploads the data to a website that permits analysis and 

comparison with other buildings. This system was used to access monthly gas consumption 

(NG).

The Tekmar systems were configured to collect 1-minute data using a Mac Mini server. The 

sensors were read every few seconds and averaged into 1-minute intervals. Boiler plant operation 

percentage indicates the runtime of the boiler. Since the boilers are single stage, runtime is 

proportional to capacity.

Temperature sensors log the temperature in individual boiler returns in Building 55 (TBR55), 

where the two boilers have built-in bypass valves to mix hot water from the boiler outlet with 

colder return water from the system prior to entry to the boilers when needed to prevent 

condensation of flue gases in the boiler. When this return temperature is higher than the main 

building return temperature, it indicates that the valve is open, allowing water to flow from boiler 

supply directly into the return.

4.5 Equipment

Table 6 describes the materials and equipment being used to collect the data described above.

Data 

Point 

Name

Description Location

(see Figure 11)

Engineering 

Units

TZj Temperatures in apartments using standalone 

sensors

12 sensors in 

3 apartments °F

TBS55 Temperature of hot water supplied to building S °F

TBR55 Temperature of hot water returned from 

building R °F

TAO55 Temperature outdoor air (used by controller) Outside building °F

TBT55 Target supply temperature for boilers Set point °F

SB55 Boiler plant operation percentage 0, 50, 100 %

NG Natural gas usage from monthly utility billing Wegowise therms
16

Table 6. Materials and Equipment

Measurement Equipment

Space Temperatures,

15-Minute Intervals

Temperature sensors in each apartment 

(Building 3) connected to wireless 

communications network; 42 standalone 

temperature sensors in 12 apartments

Outdoor Temperature

Data logged via Internet-enabled controller 

(Building 3) or via standalone data logger 

(Buildings 4 and 55)

Boiler Runtimes

Mixing Valve Position,

15-Minute Intervals

Boiler Firing Times

Supply, Return, and Boiler Water 

Temperatures

Return Boiler Water Temperatures, 

Building 55 Data collected via standalone data logger

Natural Gas Usage Utility bills via Wegowise.com
17

5 Methodology

In order to estimate the reduction in heating energy consumption due to the new control strategies, 

a regression technique was used. Regression is a statistical technique that estimates the dependence 

of a variable of interest (such as energy consumption) on one or more independent variables, such 

as ambient temperature, and can be used to estimate the effects on the dependent variable of a 

given independent variable while simultaneously controlling for the influence of other variables. 

This procedure can also be used to provide a deeper understanding of how and when energy is used. 

In order to obtain accurate predictions, the sample of energy data used for a regression model 

should be representative of the overall heating season. For energy consumption, the baseline 

modeling period should cover most of the full range of operating conditions. For this project, we 

obtained monthly energy consumption data from energy bills that differ month-to-month, not 

only because of the weather, but also because the number of days in the months may differ. To 

cope with this situation, we divided total energy use (dependent variable) in each month by the 

number of days in each month to obtain the average therms per day. Note that linear regression 

assumes that the x-values (outdoor temperatures) are known exactly, with no measurement 

errors. There are various types of linear regression models used for estimating energy 

consumption or savings. In this work, a three-parameter heating change point model was used as 

described below (Regression for M&V: Reference Guide, 2011).

Typically multifamily buildings with central heating systems provide space heating only if the 

outdoor air temperature (OAT) falls below a certain threshold known as the WWSD. Therefore, 

heating energy use may be proportional to ambient temperature, yet only below the WWSD 

temperature; if the OAT goes above the WWSD, fuel use does not continue to decrease. Energy 

associated with DHW is assumed to be similar across all seasons. Under these circumstances, a

three-parameter change-point linear regression has a better fit than a simple regression model. 

Because of the physical characteristics of buildings, the data points have a natural two-line 

angled pattern to them. 

The following equation was used to calculate energy consumption using a three-point model: 

Y = Yc + m × (T – Tc)

-

Y = The value of the dependent variable (energy use or boiler runtime) 

Yc = Temperature-independent energy use or boiler runtime

m = The linear dependence on the independent variable (slope)

T = The value of the independent variable (ambient temperature)

Tc = Change-point temperature

(T - Tc)- = Indicates that the values of the parenthetic term are set to 0 when they 

are positive

Pre- and post-retrofit boiler controller specifications are provided in Table 2. The change point 

temperature (Tc) was as assumed to be 65°F for all buildings pre-retrofit (the WWSD) and for 

buildings 4 and 55 post-retrofit. Tc was taken as 55°F for Building 3 post-retrofit daytime and 

45°F nighttime (the post-retrofit WWSD for Building 3). Ambient hourly temperature data are

used from the Logan Airport weather station in Boston, Massachusetts.

The above method was used for utility bill analysis and boiler run time analysis using hourly 

boiler run time data.
18

6 Results and Analysis

Each building was operated in multiple control states during the monitoring period. This section 

describes the control strategies employed, their effect on system operation, and their impact on 

boiler runtime and utility bills.

6.1 Building 3

The new control strategy for Building 3 incorporated two major changes from the previously 

existing controller.

1. Reset schedule: The original controller was set for supply temperature of 180°F at 0°F 

outdoor temperature, decreasing linearly to 145°F at 70°F outdoor temperature (reset 

ratio of 0.5) with a WWSD of 65°F for both day and night. The new controller was set 

for daytime to a return temperature of 170°F at 10°F outdoors, and decreasing linearly to 

130°F at 50°F outdoors (reset ratio of 1.0) with a WWSD of 55°F. For nighttime, the 

return water temp and WWSD were 10°F lower (Figure 12). Controlling to return instead 

of supply water temperature is thought to result in a more stable system.

Figure 12. Building 3 controller reset schedules

2. Indoor cutoff: The new controller can shut off the boilers when the average indoor 

temperature from apartment temperature sensors is above a desired set point (established 

as 73°F daytime and 68°F nighttime). This function was enabled and disabled a few times 

to collect data in both modes to observe the effect on boiler run time. Table 7 shows data 

collection period for various control settings.
19

Table 7. Data Collection Periods for Building 3

6.1.1 Analysis of the Impact of Indoor Cutoff

A bin analysis approach was used to estimate energy savings due to indoor cutoff. We summed 

boiler runtime as recorded by the controller for the periods when indoor cutoff was enabled and 

compared it to periods when it was disabled. 

It should be noted that there were periods when one of the three boilers (Boiler C) was running 

uncontrollably for a few hours nonperiodically. The data from those periods were removed (plus 

6 hours following to eliminate lag effects) before performing the analysis. The reason for this 

error in the boiler control system is unknown. These periods are presented in Appendix A.

We analyzed daytime and nighttime separately because they had different indoor cutoff 

thresholds and different reset curves. We also looked at the number of hours the system was in 

indoor cutoff mode in day and night. The results of this analysis are shown in Table 8.

Overall, indoor cutoff reduced boiler runtime by 28%. Because these are single stage boilers, 

fuel consumption would be proportional to the boiler run time. Nearly all savings was obtained at 

night. It was found that the indoor cutoff function was no better than ORC at reducing boiler run 

time during daytime which implies that the ORC curve selection was appropriately adjusted for 

this building for daytime operation. It is unknown if more aggressive nighttime ORC settings 

(i.e. larger nighttime setback) would have reduced boiler run time as much as indoor cutoff did at 

night. A lower daytime indoor cutoff set point may have reduced boiler run time further, but may 

also have reduced temperatures in some apartments below acceptable levels. Figure 13 shows the 

weather adjusted load lines for indoor cutoff on and off at night time only; each point represents

approximately 1 week. Table 9 shows the respective equations and correlation factors.

Time Period Indoor Cutoff Duration

February 17 – April 4, 2012 No 1.5 months

April 5 – May 24, 2012 Yes 2 months

May 25 – October 18, 2012 No heating

October 18 – November 30, 2012 Yes 1.5 months

December 1 – December 31, 2012 No 1 month

January 1 – March 31, 2013 Yes 3 months

April 1 – May 28, 2013 No 2 months

May 28 – October 1, 2013 No heating

October 1 – December 31, 2013 No 3 months
20

Table 8. Building 3 Effect of Indoor Cutoff on Boiler Runtime

Figure 13. Building 3 dependence of boiler runtime on indoor temperature 

cutoff at night only

(diamonds and green line = indoor cutoff activated; 

squares and brown line = indoor cutoff deactivated)

Table 9. Dependence of Boiler Runtime on OAT With and Without

Indoor Temperature Cutoff Enabled for Building 3

Table 10 shows average indoor air temperature with and without indoor cut off enabled. Indoor 

cutoff reduced the average indoor temperature by 0.5°F in both day and night. Average indoor 

temperatures for the entire period are shown in Figure 14. Average temperatures were in excess 

of minimum required temperature for most of the season. No comfort complaints were relayed 

by building management. The few dips in temperature data in January, early March, and early 

December 2013 were the result of brief communication failures in which data were lost.

Time

Change in Boiler Runtime 

Compared to When Indoor 

Cutoff Was Disabled (All 

Other Settings Identical)

Hours Indoor Cutoff Mode 

Was Preventing Boiler 

Operation as a Percent of 

Hours When Indoor Cutoff 

Was Enabled

Indoor 

Cutoff 

Temperature 

Threshold

Day –1% 7% 73°F

Night –71% 65% 68°F

Overall –28% 26%

Indoor Cutoff Status Equation R2

Enabled Boiler runtime = –0.202 × OAT + 0.645 0.69

Disabled Boiler runtime = –0.329 × OAT + 0.81 0.74

Load line (Indoor Cutoff on)

Load line (Indoor Cutoff off)
21

Table 10. Building 3 Average Indoor Temperature 

With and Without Indoor Cutoff Enabled

Figure 14. Building 3 average indoor temperature (summer data removed)

Indoor temperature data for all apartments obtained from control system apartment temperature 

sensors are shown in Appendix B. Temperatures ranged from the 50s to the low 90s. The 

extremely low temperatures were primarily from apartments during vacancies when the heat was 

turned off; but also in some locations when windows were opened. 

6.1.2 Utility Bill Analysis

The purpose of the utility bill analysis in Building 3 was to estimate the overall weather-adjusted 

change in space heating energy use of pre- and post-retrofit periods, recognizing that some of the 

post-retrofit period included indoor cutoff and some did not. Greater energy savings can be 

expected if indoor cutoff would be on all the time.

ARIES obtained gas billing data for the pre-retrofit season (September 2009 to May 2011) and 

the post-retrofit season (February 2012 to December 2013). The analysis method described in 

Section 5 was applied after making adjustments to account for the time Boiler C was running 

uncontrollably. The boiler runtime in those periods (Table 1, Apendix) were converted to energy 

by multiplying them by the boiler's input capacity (175,000 Btu/h). The energy obtained in these 

periods were subtracted from the relevent energy bills. Similarly, the periods were also 

substracted from the periods of the relevant energy bills and then total therms per hour were 

calculated to perform the analysis.

Indoor Cutoff Status Day Night

ON 71.6°F 71.4°F

OFF 72.1°F 71.9°F
22

Figure 15 shows the dependence of space heating energy consumption in the building on outdoor 

air temperature for pre- and post-retrofit periods. Table 11 shows the equations of the load lines 

and values of coefficients of determination for pre- and post-retrofits periods. Total predicted 

savings from the retrofit for Building 3 are presented in Table 12.

Table 11. Building 3 Dependence of Energy Consumption on OAT (Slope) 

and Coefficient of Determination (R2

) for Building 3

Figure 15. Building 3 dependence of energy consumption on OAT

(diamonds and green line = post-retrofit; squares and brown line = pre-retrofit)

Period Equation R2

Pre-Retrofit (2010–2011) Gas use = –1.381 × OAT + 10.349 0.972

Post-Retrofit (2011–2012) Gas use = –1.522 × OAT + 10.660 0.951
23

Table 12. Building 3 Pre- and Post-Retrofit Heating and Total Energy Consumption

and Reduction (therms) for Building 3

6.2 Building 4

The new control strategy for Building 4 incorporated two major changes from the previously 

existing controller.

1. Reset schedule: The original controller was set for a supply temperature of 180°F at

10°F outdoor temperature, decreasing to the 130°F minimum boiler supply temperature at 

about the 48°F outdoor temperature observed on April 7, 2011 (reset ratio of 0.8). The 

new controller was set to 180°F at 10°F outdoors (increased to 15°F partway through the 

first winter) with the same curve characteristics as the previous controller; however with 

a 2°F lower indoor target temperature (70°F instead of 72°F). The minimum boiler 

supply temperature was set at 140°F and reduced to 110°F over the course of the season

(Figure 16) (reset ratio of 1.5). As discussed in Appendix B the 110°F minimum 

temperature was judged to be acceptable.

2. Nighttime setback: The new controller incorporated nighttime setback, implemented on

March 23, 2012 with a 5°F setback. Because boiler runtime data was deemed unreliable

(there were large portions of garbage data for unknown reasons), the analysis of 

nighttime setback for Building 4 was not conducted.

Figure 16. Building 4 controller reset schedules

6.2.1 Utility Bill Analysis for Building 4

The purpose of the utility bill analysis in Building 4 was to estimate the overall weather-adjusted 

change in space heating energy use of pre and post-retrofit periods as a result of the change in 

reset schedule. It is recognized that some of the post-retrofit period included nighttime setback 

100

120

140

160

180

0 10 20 30 40 50 60 70 80

System Water 

Temperature

OAT

Supply Temp Old

WWSD New and Old

Supply Temp New

Heating Total 

(Including DHW Gas)

Pre-Retrofit (2010–2011) 10,850 14,700

Post-Retrofit (2011–2013) 

(Normalized With 2010–2011 OAT) 7,500 11,350

Total Reduction 3,350 4,001

% Reduction 30.9% 22.8%
24

and some did not; however, nighttime setback had little effect in Building 55, as will be 

described below.

ARIES obtained gas billing data for Building 4 for each month for the pre-retrofit season (May 

2010 to May 2011) and the post-retrofit season (June 2011 to May 2012). Figure 17 shows the 

dependence of space heating energy consumption in the building on outdoor air temperature for 

each season. Based on the three parameter change point linear regression model described 

earlier, the slopes and intercepts for the buildings’ energy usage were determined (Table 13). In 

order to estimate energy savings due to the new control systems and the various control 

strategies, energy consumption for each heating season was weather-normalized with weather 

data for the 2010–2011 season by applying the post-retrofit regression equation to the pre-retrofit 

(2010–2011) weather data.

Figure 17. Building 4 dependence of energy consumption on OAT

(diamonds and green line = post-retrofit; squares and brown line = pre-retrofit)

Table 13. Building 4 Dependence of Energy Consumption on OAT 

and Coefficient of Determination (R2

) for Building 4

Table 14 presents heating energy and total energy use before and after the retrofit for Building 4. 

Heating energy use decreased by about 10% after implementing the new boiler controls. Figure 

18 shows indoor temperatures in Building 4. Temperatures are well above 70°F, indicating an 

opportunity for further savings by lowering the reset curve.

Period Equation R2

Pre-Retrofit (2010-11) Gas use = –1.357 × OAT – 4.300 0.9699

Post-Retrofit (2011-12) Gas use = –1.259 × OAT – 5.081 0.9652
25

Table 14. Building 4 Pre- and Post-Retrofit Heating and

Total Energy Consumption and Reduction (therms)

Figure 18 Indoor air temperature profiles in various apartments in Building 4

6.3 Building 55

The new control strategy for Building 55 incorporated two major changes from the previously 

existing controller.

1. Reset schedule: The original Weil McClain controller is assumed to have had the same 

settings found in Building 3: Supply temperature of 180°F at 0°F outdoor temperature, 

decreasing to 145°F at 70°F outdoor temperature (reset ratio of 0.5). The new controller 

was set to be 180°F at 10°F outdoors and 70°F at 70°F outdoors with a minimum boiler 

supply temperature of 110°F for most of the winter (Figure 19) (reset ratio of 1.8).

2. Nighttime setback: The new controller incorporated nighttime setback, implemented on 

February 2, 2012 with a 10°F setback and then adjusted to a 5°F setback on February 8, 

2012 because of a perceived risk of potential comfort problems.

Table 15 shows the data collection periods used to assess the effect of nighttime setback.

Heating Total

(Including DWH Gas)

Pre-Retrofit (2010–2011) 6,800 11,150

Post-Retrofit (2011–2013) 

(Normalized With 2010–2011 OAT) 6,100 10,150

Total Reduction 700 1,023

% Reduction 10.1% 9.1%
26

Figure 19. Building 55 controller reset schedules

Table 15. Data Collection Periods for Building 55

6.3.1 Boiler Runtime—Gas Use Correlation

The data in Figure 20 and Table 16 show that the gas use predicted from boiler runtime is in 

good agreement with the actual measured gas use (from the utility bills).

100

110

120

130

140

150

160

170

180

190

200

0 20 40 60 80

System Water Temperature

OAT

Supply Temp Old

WWSD New and Old

Supply Temp New

Time Period Nighttime 

Setback Duration

September 27, 2011 – February 1, 2012 No 4 months

February 2 – May 24, 2012 Yes 4 months

May 25 – October 18, 2012 No heating

October 18, 2012 – May 28, 2013 Yes 7.5 months

May 28 – October 1, 2013 No heating

October 1 – November 18, 2013 Yes 1.5 months

November 19 – December 31, 2013 No 1.5 months
27

Figure 20. Plot of actual and predicted gas use

Table 16. Actual and Predicted Gas Use by Utility Billing Period for Building 55

Note: Predicted using 1.75 MMBtu/h and assuming that DHW is 4.5 therms/day

6.3.2 Boiler Runtime Analysis

Boiler runtime data shows little impact of the nighttime setback. In Figure 21, boiler loading on 

the non-setback days (red symbols) falls very close to the trend line for setback days (green 

symbols). Nighttime setback was applied from February 2, 2012 to November 18, 2013. There 

were periods before and after the nighttime setback period when only WWSD was applied (i.e.,

no nighttime setback). However, WWSD was changed from 65°F to 60°F on November 30,

2012. In order to estimate energy savings for nighttime setback, data were separated for the two 

different WWSD settings. Based on the three parameter change point linear regression model 

described earlier, the slopes and intercepts for each case were determined (Table 17). Data are 

weather normalized.

200 400 600 800 1000

Actual Gas Use (therms)

200

400

600

800

1000 Predicted Gas Use (therms)

Period

Actual/Measured 

Gas Use

(therms)

Predicted Gas 

Use

(therms)

April 26, 2012 – May 25, 2012 334 312

March 25, 2012 – April 25, 2012 537 508

February 28, 2012 – March 24, 2012 556 523

January 27, 2012 – February 27, 2012 866 858

December 28, 2011 – January 26, 2012 942 923

November 26, 2011 – December 27, 2011 714 733

October 25, 2011 – November 25, 2011 594 494

Predicted = [1.75 therms/h] × [boiler hours] + 

[4.5 therms/day] × [days]
28

Figure 21. Plot of boiler loading—comparing days with and without setback 

through January 31, 2014 for Building 55

(red = no setback, green = with setback)

Table 17. Dependence of Boiler Runtime on Nighttime Setback (Slope)

and Coefficient of Determination (R2

) for Building 55

Figure 22 Shows apartment temperatures during four months of the 2013 heating season. Most 

spaces were well above minimum comfort temperatures. No complaints were recorded. It is 

possible that an even more aggressive reset ratio could have been used to generate greater 

savings.

0%

40%

80%

120%

160%

200%

0 10 20 30 40 50 60 70 80 90 100

Boiler Loading (%)

Outdoor Air Temperature (F)

WWSD Period Equation R2

65

No setback (2010–2011) Boiler runtime = –0.026 × OAT – 0.095 0.95

With setback (2011–2012) Boiler runtime = –0.020 × OAT + 0.044 0.99

60

No setback (2010–2011) Boiler runtime = –0.052 × OAT – 0.577 0.99

With setback (2011–2012) Boiler runtime = –0.020 × OAT + 0.090 0.99
29

Figure 22. Indoor air temperature profiles in various apartments in Building 55

6.3.3 Utility Bill Analysis

As with Building 4, the purpose of the utility bill analysis in Building 55 was to estimate the 

overall weather-adjusted change in space heating energy use of pre and post-retrofit periods as a 

result of the change in reset schedule. It is recognized that some of the post-retrofit period 

included nighttime setback and some did not, however, as described above, nighttime setback 

had little effect.

ARIES obtained gas billing data for the pre-retrofit season (May 2010 to May 2011) and the 

post-retrofit season (June 2011 to December 2013). Figure 23 shows the dependence of space 

heating energy consumption in the building on outdoor air temperature for each season. Based on 

the three parameter change point linear regression model described earlier, the slopes and 

intercepts for all three buildings’ energy usage were determined (Table 18). 

In order to estimate energy savings due to the new control systems and the various control 

strategies, energy consumption for each heating season was weather-normalized with 2010–2011 

OATs. The graphs show that post-retrofit (the green load line) less fuel was required for space 

heating at a given OAT compared to pre-retrofit (the red load line). Total predicted savings from 

the retrofit are shown in Table 19. Heating energy use decreased by approximately 15% after 

implementing the new boiler controls.
30

Figure 23. Building 55 dependence of energy consumption on OAT

(diamonds and green line = post-retrofit; squares and brown line = pre-retrofit)

Table 18. Dependence of Energy Consumption on OAT (Slope)

and Coefficient of Determination (R2

) for Building 55

Table 19. Pre- and Post-Retrofit Heating and Total Energy 

Consumption and Reduction (therms) for Building 55

Figure 24 shows the dependence of space heating energy consumption in the building on 

nighttime setback for each season. It can be seen in the figure that, consistent with the boiler run 

time analysis, little to no impact of the nighttime setback was observed.

Period Equation R2

Pre-Retrofit (2010–2011) Gas use = –0.699 × OAT + 6.215 0.9924

Post-Retrofit (2011–2012) Gas use = –0.883 × OAT – 1.076 0.9973

Heating Total

Pre-Retrofit (2010–2011) 5,750 7,900

Post-Retrofit (2011–2012) (Normalized 

With 2010–2011 OAT) 4,850 7,000

Total Reduction 900 900

% Reduction 15.5% 10.5%
31

Figure 24. Building 55 dependence of energy consumption on night time setback

(diamonds and green line = post-retrofit; squares and brown line = pre-retrofit)

6.4 Cost Effectiveness

Table 20 summarizes the major statistics for each building, including space heating gas use, costs 

of boiler controls and communications infrastructure, and simple payback. It can be seen in the 

table that the billing analysis based on 2011 onwards utility bills (with the new control systems) 

and 2010-2011 utility bills (with old control system) showed that the new control system saved a 

significant amount of space heating energy. After implementing control modifications, the 

overall weather-adjusted reduction in space heating gas consumption was 10.1% to 30.9% and 

averaged 19%. Nighttime setback of supply water temperature by 5°F had a negligible in the two 

buildings in which it was tested. On the other hand, indoor temperature cutoff based on average 

apartment temperatures showed significant benefits in one building, reducing boiler run time by 

28%. Overall, the control strategies applied in these buildings are promising and show significant 

energy savings with simple paybacks of 1.6 to 4.2 years.
32

Table 20. Energy and Cost Savings Calculations for Heating Season 2011–2012

6.5 Uncertainties

Two types of analysis were conducted in this project: one based on boiler runtime data and one 

based on utility bills. In the boiler runtime analysis, boiler runtime was measured to estimate 

energy savings before and after implementing nighttime setback and indoor cutoff. In this 

analysis, we ignored the uncertainties associated with indoor and outdoor air temperature 

measurements, which could have some effect on the boilers operation under these control 

strategies. There is assumed to be no error in the boiler runtime data because these data were 

taken directly from the boiler commands. However, there may be uncertainty in the input 

capacity of the boilers, which may also vary depending on density of the gas supplied. The OAT

data used for weather normalization were obtained from a nearby weather station; this sensor 

was assumed to be accurate.

In the utility bill analysis, all utility bills obtained for Buildings 4 and 55 were actual meter 

readings, and therefore no uncertainty is involved in that data. For Building 3, there were some

estimated readings which were combined with the subsequent month actual readings. This 

resulted in some longer actual gas usage periods (60 or 90 days), and fewer data points, but the 

Building Information Building 3 Building 4 Building 55

Number of Apartments Served 18 15 8

Number of Bedrooms 45 40 28

Floor Area 15,524 ft2 13,672 ft2 9,955 ft2

Control Strategy Implemented ORC and

indoor cutoff ORC

ORC and

nighttime 

setback

Gas Use for Space Heating

Post-Retrofit (annualized) 7,500 therms 6,100 therms 4,850 therms

Pre-Retrofit 2010–2011 10,850 6,800 5,750

Economics (gas rate of $1.22 per therm)

Savings (% space heating gas) 30.9% 10.1% 15.5%

Savings 3,350 therms 700 therms 900 therms

Savings $4,150 $850 $1,100

Equipment Costs $10,610 $1,235 $580

Labor Costs $6,750 $1,200 $1,200

Total Costs $17,360 $2,435 $1,780

Cost per Apartment $964 $162 $222

Payback 4.2 yr 2.9 yr 1.6 yr

Internal Rate of Return (Assuming 3% 

Annual Escalation in Energy Costs) 17% 32% 62%
33

correlation coefficient remained acceptable. Gas utility bills also include uncertainty as to gas 

usage for space heating relative to water heating or cooking in addition to using the average 

monthly total to estimate daily gas use.

6.6 Other Impacts

The retrofit control systems are expected to provide other nonenergy benefits to building 

residents and management, including:

• Occupant comfort: The heating control system is intended to improve comfort by 

ensuring adequate heat and reducing overheating of apartments. Achieving optimum 

savings may result in some apartments being cooler than occupants have become 

accustomed to, but still within the legal limits (Gifford 2004). A survey was conducted to 

gauge occupant satisfaction with the heating system, and repeated in 2012–2013. Very 

few responses were received (a total of fewer than 10 for both surveys combined). One 

way of interpreting the lack of response is to assume that residents had few complaints 

about the heat (very few complaints were made to management as well). The indoor 

temperature data also bear this out; most apartments remained warmer than 70°F 

throughout the winter. Because no indoor temperature data are available from prior to the 

retrofit, it is unknown if temperatures were reduced as a result of the new controllers.

• Occupant health and safety: Reducing overheating reduces drying of indoor air and 

limits the need to open windows to allow excess heat to escape in winter.

• Code compliance: The system employed in Building 3 enables the property manager to 

more precisely and reliably comply with minimum/maximum heat laws for rental 

apartments through online monitoring and logging of individual apartment temperatures.

• Building and equipment maintainability: Web-enabled visibility of apartment 

temperatures (Building 3) and boiler/valve status permits maintenance personnel to more 

rapidly detect and react to maintenance issues and complaints.
34

7 Conclusions

This report summarizes the results of a boiler controls retrofit in three buildings in Cambridge, 

Massachusetts. Controllers in all three buildings were replaced and the data collected for two

heating seasons. The following research questions were addressed:

Question 1: What are the energy and comfort impacts of retrofitting multifamily central boiler 

controllers using ORC?

Answer: A billing analysis comparing 2011 onward utility bills (with new control system) and 

2010–2011 utility bills (with old control systems) showed that the new control systems saved a 

significant amount of space heating energy. The overall weather-adjusted reduction in space 

heating gas consumption was 10%–15% from improvements to ORC. The simple average 

payback is projected to be less than 3 years. Indoor air temperature data obtained from 

temperature sensors mounted in various apartments in each building indicate that the temperature 

was generally within the comfort range throughout the buildings for the entire winter. These 

results lead to the conclusion that boiler control improvements in hydronically heated buildings 

can be a cost-effective and simple-to-implement strategy with large potential energy savings.

Question 2: How does a control system incorporating apartment temperature data compare in 

cost and performance with well-tuned ORC strategies?

Answer: An indoor temperature cutoff control feature based on average apartment temperatures 

showed significant benefits in the one building where it was tested. Indoor cutoff control reduced 

boiler runtime by 28%. Nearly all these savings were obtained in the night, which had lower 

indoor temperature cutoff (68°F) compared to the day (73°F). This implies that the ORC curve 

selection was appropriately adjusted for this building for daytime operation and that ORC alone 

can potentially achieve similar results if properly tuned to the building.

Question 3: What is the impact on space heating energy consumption of nighttime setback of 

supply water temperature on multifamily buildings with central hydronic space heating? 

Answer: The data show negligible effect of a 5°F nighttime setback of supply water temperature 

in the one building in which results were obtained. It may be that 5°F was too small a setback 

and that the building mass dampened the effect of the setback.
35

References

Center for Energy and the Environment. (2006). Outdoor Reset and Cutout Control. Center for 

Energy and Environment: www.mncee.org/pdf/multifamily/outdrreset_coutoutcon.pdf.

CNT Energy. (2010). Boiler Controls and Sensors. Retrieved April 2011, from Energy Savers « 

CNT Energy: www.cntenergy.org/media/Boiler-Controls-Sensors.pdf.

Gifford, H. (2004). Choosing Thermostatic Radiator Valves. American Boiler Manufacturer’s 

Association.

Hewett, M.J.; Peterson, G.A. (1984). “Measured Energy Savings From Outdoor Resets in 

Modern, Hydronically Heated Apartment Buildings.” Proceedings of the American Council for 

an Energy Efficient Economy 1984 Summer Study C:135–152. Washington. D.C.: ACEEE.

Liao, Z.; Dexter, A. (2004). “The Potential for Energy Saving in Heating Systems Through 

Improving Boiler Controls.” Energy and Buildings 261–271.

Liao, Z.; Dexter, A. (2005). “An Experimental Study on an Inferential Control Scheme for 

Optimising the Control of Boilers in Multi-Zone Heating System.” Energy and Buildings 55–63.

Peterson, G. (1986). “Multifamily Pilot Project Single Pipe Steam Balancing Hot Water Outdoor 

Reset.” Proceedings of the American Council for an Energy Efficient Economy 1986 Summer 

Study 1:140–154. Washington, D.C.: ACEEE.

U.S. Census Bureau. (2006). American Housing Survey for the United States: 2005. Washington 

D.C.: U.S. Government Printing Office.

Massachusetts Department of Public Health. (2012, 06 10). MINIMUM STANDARDS OF 

FITNESS FOR HUMAN HABITATION. Retrieved 11 12, 2012, from The Official Website of 

the Commonwealth of Massachusetts: 

www.mass.gov/eohhs/docs/dph/regs/105cmr410.pdf#page=7.

Regression for M&V: Reference Guide. (2011). Version 1.0. Prepared for Bonneville Power 

Administration: www.bpa.gov/energy/n/pdf/BPARegression_forMV_ReferenceGuide.pdf.
36

Appendix A

Table 21. Periods in Which One of the Boilers in Building 3 Was Running Uncontrollably

CAST Boiler C Stuck in ON Position These Times

From Time To Time Hours to 

Discard

11/6/2012 8:30 a.m. 11/6/2012 9:04 p.m. 11.97

11/9/2012 9:48 a.m. 11/9/2012 5:15 p.m. 7.08

11/17/2012 10:14 a.m. 11/17/2012 1:45 p.m. 3.07

11/19/2012 4:53 p.m. 11/19/2012 7:19 p.m. 1.87

11/25/2012 7:02 p.m. 11/25/2012 10:18 p.m. 2.85

11/26/2012 10:24 a.m. 11/26/2012 6:47 p.m. 8.30

12/23/2012 5:45 a.m. 12/23/2012 10:16 p.m. 16.40

12/28/2012 11:22 a.m. 12/30/2012 11:59 p.m. 60.73

12/30/2012 11:59 p.m. 12/31/2012 1:00 p.m. 12.72

1/1/2013 8:50 a.m. 1/1/2013 10:13 p.m. 13.20

1/2/2013 11:44 a.m. 1/2/2013 5:56 p.m. 6.28

1/4/2013 9:03 a.m. 1/4/2013 10:12 p.m. 13.65

1/8/2013 4:50 p.m. 1/8/2013 10:20 p.m. 5.57

1/10/2013 7:59 a.m. 1/10/2013 4:58 p.m. 8.75

1/19/2013 8:58 a.m. 1/19/2013 10:09 p.m. 13.05

1/20/2013 7:10 p.m. 1/20/2013 8:40 p.m. 1.55

1/28/2013 6:45 a.m. 1/28/2013 10:13 p.m. 16.22

2/1/2013 7:02 a.m. 2/1/2013 10:09 p.m. 15.13

2/4/2013 8:29 p.m. 2/4/2013 10:07 p.m. 1.78

2/6/2013 9:39 a.m. 2/6/2013 10:09 p.m. 12.50

2/8/2013 5:54 a.m. 2/8/2013 10:09 p.m. 16.28

2/17/2013 9:46 a.m. 2/17/2013 10:29 p.m. 13.47

2/19/2013 1:01 p.m. 2/19/2013 7:49 p.m. 6.05

2/25/2013 6:53 p.m. 2/25/2013 9:18 p.m. 2.08

2/28/2013 9:46 a.m. 2/28/2013 3:56 p.m. 6.20

3/1/2013 6:44 a.m. 3/4/2013 11:27 a.m. 76.30

3/6/2013 1:06 p.m. 3/6/2013 8:56 p.m. 7.57
37

CAST Boiler C Stuck in ON Position These Times

From Time To Time Hrs to 

discard

3/15/2013 10:14 a.m. 3/15/2013 10:17 p.m. 11.22

3/16/2013 7:13 a.m. 3/16/2013 9:49 p.m. 14.42

4/9/2013 5:28 a.m. 4/9/2013 9:53 a.m. 4.08

4/14/2013 6:41 p.m. 4/14/2013 11:20 p.m. 4.72

11/5/2013 5:00 a.m. 11/5/2013 10:00 p.m. 16.90

11/12/2013 10:00 p.m. 11/14/2013 10:00 p.m. 47.47

11/15/2013 5:00 p.m. 11/19/2013 3:00 p.m. 94

11/19/2013 8:00 p.m. 11/20/2013 6:00 p.m. 20.37

11/21/2013 1:00 p.m. 11/21/2013 6:00 p.m. 4.53

11/25/2013 11:00 p.m. 11/26/2013 1:00 p.m. 13.35

12/3/2013 11:30 a.m. 12/3/2013 11:00 p.m. 10.72

12/5/2013 6:00 p.m. 12/5/2013 10:00 p.m. 4.17

12/10/2013 10:00 p.m. 12/11/2013 5:00 a.m. 7.30

12/22/2013 5:00 a.m. 12/23/2013 10:00 p.m. 16.08

1/20/2014 5:00 a.m. 1/21/2014 5:00 a.m. 24.00
38

Appendix B

This appendix presents sample data freom each building, illustrating controller operation and 

effects. 

Building 3

Figure 25 shows the system temperature for the three sample periods with Intech control in 

which different control settings were implemented:

• January 1 to February 16, 2012 (black crosses): During this period, ORC was not 

enabled. The system was operated with a fixed return water temperature that differed 

only during daytime (180°F) and nighttime (170°F) due to a nighttime setback. The two 

black horizontal groups of data points reflect these two system set points. 

• February 17 to April 4, 2012 (red stars): ORC was enabled on February 17, 2012. The 

system temperatures are significantly lower during this period. However, because a 

number of other adjustments to controller settings were made during February and 

March, a clear trend line is not evident.

• April 5 to May 31, 2012 (blue diamonds): During this period, control settings were 

fixed and indoor cutoff was added. A trend line is evident, indicating the ORC. Many 

data points are at the bottom of the plot during this period because of the greater 

incidence of WWSD during this time of year.

Figure 26 shows the system temperature compared to the average indoor temperature from 

the apartment temperature sensors. The red data points (stars) are from the period prior to 

indoor cutoff; the blue data points (diamonds) are during indoor cutoff. The lack of blue data 

points to the right of approximately 74°F indoor temperature confirms that this function was 

operating: there is no boiler operation above 74°F average indoor temperature. Indoor 

temperature cutoff was set at 73°F during the day and 68°F at night, with a 1°F dead band.

Figure 27 and Figure 28 show the range and fluctuation of apartment temperatures with and 

without indoor cutoff activated. 
39

Figure 25. Building 3 system temperature versus outdoor temperature

Figure 26. Building 3 system temperature versus average indoor temperature

Intech, Bldg 3 - 02/01/12 to 05/31/12

0 20 40 60 80 100

Outdoor Temperature (F)

60

80

100

120

140

160

180

System Temperature (F)

Jan 1 to Feb 16 (Constant Temp)

Feb 17 to Apr 4 (Reset)

Apr 5 to May 31 (Reset w/Indr Cutoff)

Intech, Bldg 3 - 02/01/12 to 05/31/12

60 65 70 75 80

Indoor Temperature from Host (F)

60

80

100

120

140

160

180

System Temperature (F)
40

Figure 27. Building 3 apartment temperatures from control system sensors: October 2012 – May 2013; with indoor cutoff activated

Figure 28. Building 3 apartment temperatures from control system sensors:

October 2013 – December 2013; indoor cutoff mode deactivated
41

Building 4

In Building 4 the boiler supply and return temperatures were set to a higher temperature (above 

140°F) because the mixing valve controlled the system temperature. However, the 140°F

minimum was not always achieved and the boiler firing pattern was irregular. The cause of this 

was a conflict between the boiler controller and the mixing valve controller. The root of this 

problem was that the recirculation pumps turn on and off with the boiler. An alternate approach 

that could have successfully maintained boiler water return temperature control with the two 

Tekmar controllers would have required that the boiler pumps be re-wired back to the Tekmar 

controller.

This was resolved on February 2, 2012 by fixing the valve in the open position, disabling the 

valve controller, and putting the boiler controller in charge of controlling supply water 

temperature. This was possible because it was determined that, after reviewing return water 

temperatures, the risk to the boilers of sub-140°F return water was low because sub-140°F return 

water was very infrequent.

Prior to February 2, 2012 the system was operating at constant high temperature control without 

ORC. When the mixing valve controller was disabled and the ORC schedule implemented on 

February 2, the boiler loading was reduced. The resulting data are more organized in a linear 

trend as compared to the scattered data before that date (Figure 29). The cause of the occasional 

data above the trend line is unknown.

The ORC schedule (after February 2, 2012) had a noticeable impact on gas use as compared to 

constant high temperature. In Figure 30, the solid line is a best fit of data from August 2011 to 

January 2012 (before reset) and the dashed line is the best fit from February to May 2012. The 

stars are February and March (no night setback); the, triangles are April and May (with night 

setback).

Figure 29. Boiler loading, Building 4

BLDG 4 (100% = 1 Blr all day)

0 20 40 60 80 100

Avg Outdoor Temperature (F)

0

200

400 Boiler Loading (%)

Reset (after Feb 2)

No Reset
42

Figure 30. Gas use, Building 4

Nighttime Setback

Nighttime setback was implemented in March. There was little evidence of the 5°F nighttime 

setback having an impact on boiler runtime. Both the days with and the days without setback fall 

roughly along the same boiler loading trend line (Figure 31).

Figure 31. No impact of nighttime setback, Building 4

20 40 60 80

Outdoor Temperature (F)

0

10

20

30

40

50

Gas Use (therms/day)

BLDG 4 (100% = 1 Blr all day)

0 20 40 60 80 100

Avg Outdoor Temperature (F)

0

50

100

150

Boiler Loading (%)

Reset (after Feb 2)

Reset & Setback

Aug-11 to Jan-12 Feb-12 & Mar-12

Apr-12 & May-12
43

Building 55

The plot of the supply target temperature versus outdoor temperature (Figure 32) illustrates the 

reset schedule. The second shorter line below the primary line is a result of the week with 10°F 

night setback.

Figure 32. Boiler (system) supply target and outdoor temperature in Building 55

Bldg 55

30 40 50 60 70 80

Outdoor Temperature (F)

100

120

140

160

Boiler Supply Target (F)
DOE/GO-102014-4493 ▪ September 2014